class_docstr: LSTM
  
  LSTM encoder, with MLP decoder.
  The network has `tanh` or `relu` non-linearities, it is trained using
  ADAM stochastic gradient descent. The network accepts static, historic
  and future exogenous data.
  
  **Parameters:**<br>
  `h`: int, forecast horizon.<br>
  `input_size`: int, maximum sequence length for truncated train backpropagation. Default -1 uses all history.<br>
  `inference_input_size`: int, maximum sequence length for truncated inference. Default -1 uses all history.<br>
  `encoder_n_layers`: int=2, number of layers for the LSTM.<br>
  `encoder_hidden_size`: int=200, units for the LSTM's hidden state size.<br>
  `encoder_activation`: str=`tanh`, type of LSTM activation from `tanh` or `relu`.<br>
  `encoder_bias`: bool=True, whether or not to use biases b_ih, b_hh within LSTM units.<br>
  `encoder_dropout`: float=0., dropout regularization applied to LSTM outputs.<br>
  `context_size`: int=10, size of context vector for each timestamp on the forecasting window.<br>
  `decoder_hidden_size`: int=200, size of hidden layer for the MLP decoder.<br>
  `decoder_layers`: int=2, number of layers for the MLP decoder.<br>
  `futr_exog_list`: str list, future exogenous columns.<br>
  `hist_exog_list`: str list, historic exogenous columns.<br>
  `stat_exog_list`: str list, static exogenous columns.<br>
  `loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>
  `valid_loss`: PyTorch module=`loss`, instantiated valid loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>
  `max_steps`: int=1000, maximum number of training steps.<br>
  `learning_rate`: float=1e-3, Learning rate between (0, 1).<br>
  `num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.<br>
  `early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.<br>
  `val_check_steps`: int=100, Number of training steps between every validation loss check.<br>
  `batch_size`: int=32, number of differentseries in each batch.<br>
  `valid_batch_size`: int=None, number of different series in each validation and test batch.<br>
  `scaler_type`: str='robust', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>
  `random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br>
  `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>
  `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>
  `alias`: str, optional,  Custom name of the model.<br>
  `**trainer_kwargs`: int,  keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>
code: def __init__(
      self,
      h: int,
      input_size: int = -1,
      inference_input_size: int = -1,
      encoder_n_layers: int = 2,
      encoder_hidden_size: int = 200,
      encoder_bias: bool = True,
      encoder_dropout: float = 0.0,
      context_size: int = 10,
      decoder_hidden_size: int = 200,
      decoder_layers: int = 2,
      futr_exog_list=None,
      hist_exog_list=None,
      stat_exog_list=None,
      loss=MAE(),
      valid_loss=None,
      max_steps: int = 1000,
      learning_rate: float = 1e-3,
      num_lr_decays: int = -1,
      early_stop_patience_steps: int = -1,
      val_check_steps: int = 100,
      batch_size=32,
      valid_batch_size: Optional[int] = None,
      scaler_type: str = "robust",
      random_seed=1,
      num_workers_loader=0,
      drop_last_loader=False,
      **trainer_kwargs,
  ):
      super(LSTM, self).__init__(
          h=h,
          input_size=input_size,
          inference_input_size=inference_input_size,
          loss=loss,
          valid_loss=valid_loss,
          max_steps=max_steps,
          learning_rate=learning_rate,
          num_lr_decays=num_lr_decays,
          early_stop_patience_steps=early_stop_patience_steps,
          val_check_steps=val_check_steps,
          batch_size=batch_size,
          valid_batch_size=valid_batch_size,
          scaler_type=scaler_type,
          futr_exog_list=futr_exog_list,
          hist_exog_list=hist_exog_list,
          stat_exog_list=stat_exog_list,
          num_workers_loader=num_workers_loader,
          drop_last_loader=drop_last_loader,
          random_seed=random_seed,
          **trainer_kwargs,
      )
  
      # LSTM
      self.encoder_n_layers = encoder_n_layers
      self.encoder_hidden_size = encoder_hidden_size
      self.encoder_bias = encoder_bias
      self.encoder_dropout = encoder_dropout
  
      # Context adapter
      self.context_size = context_size
  
      # MLP decoder
      self.decoder_hidden_size = decoder_hidden_size
      self.decoder_layers = decoder_layers
  
      self.futr_exog_size = len(self.futr_exog_list)
      self.hist_exog_size = len(self.hist_exog_list)
      self.stat_exog_size = len(self.stat_exog_list)
  
      # LSTM input size (1 for target variable y)
      input_encoder = 1 + self.hist_exog_size + self.stat_exog_size
  
      # Instantiate model
      self.hist_encoder = nn.LSTM(
          input_size=input_encoder,
          hidden_size=self.encoder_hidden_size,
          num_layers=self.encoder_n_layers,
          bias=self.encoder_bias,
          dropout=self.encoder_dropout,
          batch_first=True,
      )
  
      # Context adapter
      self.context_adapter = nn.Linear(
          in_features=self.encoder_hidden_size
          + self.futr_exog_size * h,
          out_features=self.context_size * h,
      )
  
      # Decoder MLP
      self.mlp_decoder = MLP(
          in_features=self.context_size + self.futr_exog_size,
          out_features=self.loss.outputsize_multiplier,
          hidden_size=self.decoder_hidden_size,
          num_layers=self.decoder_layers,
          activation="ReLU",
          dropout=0.0,
      )
  
file_path: neuralforecast/neuralforecast/models/lstm.py
imports: from typing import Optional
  import torch
  import torch.nn
  from ..losses.pytorch import MAE
  from ..common._base_recurrent import BaseRecurrent
  from ..common._modules import MLP
label_desc: Use image node_id 1 to create a new LSTM object from inherited base classes: BaseRecurrent with example: obj = LSTM(h, input_size, inference_input_size, encoder_n_layers, encoder_hidden_size, encoder_bias, encoder_dropout, context_size, decoder_hidden_size, decoder_layers, futr_exog_list, hist_exog_list, stat_exog_list, loss, valid_loss, max_steps, learning_rate, num_lr_decays, early_stop_patience_steps, val_check_steps, batch_size, valid_batch_size, scaler_type, random_seed, num_workers_loader, drop_last_loader)